{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g_a9QvUFVCUR"
   },
   "source": [
    "<h1>Chapter 11 - Fine-tuning Representation Models for Classification</h1>\n",
    "<i>Exploring the performance in classification of representation models.</i>\n",
    "\n",
    "<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\"><img src=\"https://img.shields.io/badge/Buy%20the%20Book!-grey?logo=amazon\"></a>\n",
    "<a href=\"https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/\"><img src=\"https://img.shields.io/badge/O'Reilly-white.svg?logo=\"></a>\n",
    "<a href=\"https://github.com/HandsOnLLM/Hands-On-Large-Language-Models\"><img src=\"https://img.shields.io/badge/GitHub%20Repository-black?logo=github\"></a>\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter11/Chapter%2011%20-%20Fine-Tuning%20BERT.ipynb)\n",
    "\n",
    "---\n",
    "\n",
    "This notebook is for Chapter 11 of the [Hands-On Large Language Models](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961) book by [Jay Alammar](https://www.linkedin.com/in/jalammar) and [Maarten Grootendorst](https://www.linkedin.com/in/mgrootendorst/).\n",
    "\n",
    "---\n",
    "\n",
    "<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\">\n",
    "<img src=\"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/images/book_cover.png\" width=\"350\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [OPTIONAL] - Installing Packages on <img src=\"https://colab.google/static/images/icons/colab.png\" width=100>\n",
    "\n",
    "If you are viewing this notebook on Google Colab (or any other cloud vendor), you need to **uncomment and run** the following codeblock to install the dependencies for this chapter:\n",
    "\n",
    "---\n",
    "\n",
    "💡 **NOTE**: We will want to use a GPU to run the examples in this notebook. In Google Colab, go to\n",
    "**Runtime > Change runtime type > Hardware accelerator > GPU > GPU type > T4**.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# !pip install datasets>=2.18.0 transformers>=4.38.2 sentence-transformers>=2.5.1 setfit>=1.0.3 accelerate>=0.27.2 seqeval>=1.2.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UBeVnXxQWy7-"
   },
   "source": [
    "## **Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365,
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    "executionInfo": {
     "elapsed": 21399,
     "status": "ok",
     "timestamp": 1719386835780,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "5phRS_z2U_3T",
    "outputId": "439a9900-6d9a-4a53-ea30-4774d3bf4de7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "model_id": "e3c168a65bd34a64b2c4d13129f9d750",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/7.46k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cd7314417ab48f99adf352f0cab863f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/699k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a4ca5b37d6f426a9d294ade901f6984",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09a021dba22548f3bd101e237c5ce51d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eaded0fbbd74c67b802fbb92973fa3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/8530 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a5d72b8d2484c54977e5ec692a00004",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91552574464449e48e54a6939f0a5d18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# Prepare data and splits\n",
    "tomatoes = load_dataset(\"rotten_tomatoes\")\n",
    "train_data, test_data = tomatoes[\"train\"], tomatoes[\"test\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xya5dfmVoR1R"
   },
   "source": [
    "## **Supervised Classification**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ODEmWQe8W_le"
   },
   "source": [
    "### HuggingFace Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
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    },
    "id": "b-1UGAg7WAHk",
    "outputId": "e1fd7d6f-5b19-4fd7-8be0-690afe164820"
   },
   "outputs": [
    {
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       "version_major": 2,
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
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      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
      ]
     },
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    },
    {
     "data": {
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       "model_id": "5c43d75618834ca49ff92c52c725dfdc",
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      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35498c89148e4e548e110ff979b33dd8",
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      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "# Load Model and Tokenizer\n",
    "model_id = \"bert-base-cased\"\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r48vDo8fa33D"
   },
   "source": [
    "Tokenize our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
     "elapsed": 5163,
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     "timestamp": 1719386850111,
     "user": {
      "displayName": "Maarten Grootendorst",
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     },
     "user_tz": -120
    },
    "id": "5ySNm-a3WCFI",
    "outputId": "892e96a5-7161-485c-b514-9e24ec117394"
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   "outputs": [
    {
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       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5637ae2cbd44f21aee434d399e2be37",
       "version_major": 2,
       "version_minor": 0
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       "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "\n",
    "# Pad to the longest sequence in the batch\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
    "\n",
    "def preprocess_function(examples):\n",
    "   \"\"\"Tokenize input data\"\"\"\n",
    "   return tokenizer(examples[\"text\"], truncation=True)\n",
    "\n",
    "# Tokenize train/test data\n",
    "tokenized_train = train_data.map(preprocess_function, batched=True)\n",
    "tokenized_test = test_data.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QJ1NM1gjbMD7"
   },
   "source": [
    "Define metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y724gUYyWIvq"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import evaluate\n",
    "\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    \"\"\"Calculate F1 score\"\"\"\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "\n",
    "    load_f1 = evaluate.load(\"f1\")\n",
    "    f1 = load_f1.compute(predictions=predictions, references=labels)[\"f1\"]\n",
    "    return {\"f1\": f1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AAm-sAl9bOPC"
   },
   "source": [
    "Train model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dho6VcG9WK5u"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "# Training arguments for parameter tuning\n",
    "training_args = TrainingArguments(\n",
    "   \"model\",\n",
    "   learning_rate=2e-5,\n",
    "   per_device_train_batch_size=16,\n",
    "   per_device_eval_batch_size=16,\n",
    "   num_train_epochs=1,\n",
    "   weight_decay=0.01,\n",
    "   save_strategy=\"epoch\",\n",
    "   report_to=\"none\"\n",
    ")\n",
    "\n",
    "# Trainer which executes the training process\n",
    "trainer = Trainer(\n",
    "   model=model,\n",
    "   args=training_args,\n",
    "   train_dataset=tokenized_train,\n",
    "   eval_dataset=tokenized_test,\n",
    "   tokenizer=tokenizer,\n",
    "   data_collator=data_collator,\n",
    "   compute_metrics=compute_metrics,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 62039,
     "status": "ok",
     "timestamp": 1719386995069,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "vOzl0WnSbVnY",
    "outputId": "0058b56f-6a7c-40d8-f08f-2bd035d7ab79"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='534' max='534' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [534/534 01:00, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.424000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=534, training_loss=0.4183677966228585, metrics={'train_runtime': 61.6658, 'train_samples_per_second': 138.326, 'train_steps_per_second': 8.66, 'total_flos': 227605451772240.0, 'train_loss': 0.4183677966228585, 'epoch': 1.0})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xkBUVlUYbUnn"
   },
   "source": [
    "Evaluate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173,
     "referenced_widgets": [
      "d656514d509441548af1dd6c7326d18a",
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      "d5d54eddd21a4999aec6ea6a7782e223",
      "e8da2d90a93b4ce9a93c2ee93f75d59b",
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      "3c38333cafb446bab3f3f75824075927",
      "960641d3e97f4a538a135861ab058bca",
      "fc4d3609cada4615924d5e92ee1ffdf4",
      "f914214414b14945a72ac57ac93dcb5e",
      "8cb0531c591c43f098564ce043eb036f"
     ]
    },
    "executionInfo": {
     "elapsed": 3959,
     "status": "ok",
     "timestamp": 1719386999026,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "wCI9uYDObWU8",
    "outputId": "5c2d54d2-0c81-4a10-d46d-9d7c4c9405d3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='67' max='67' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [67/67 00:01]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d656514d509441548af1dd6c7326d18a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.37090229988098145,\n",
       " 'eval_f1': 0.8566073102155576,\n",
       " 'eval_runtime': 3.1133,\n",
       " 'eval_samples_per_second': 342.407,\n",
       " 'eval_steps_per_second': 21.521,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o5gefwxOBllA"
   },
   "source": [
    "### Freeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1719386999026,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "t0ZOuoe7Dj3c",
    "outputId": "00b9fb9c-f819-457d-91ad-a57881778d1e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Load Model and Tokenizer\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1719386999026,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "iI8vf_mnBniu",
    "outputId": "d5d31516-977b-462b-f878-5a71c084db7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "bert.embeddings.token_type_embeddings.weight\n",
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      "bert.encoder.layer.10.intermediate.dense.weight\n",
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      "bert.encoder.layer.10.output.LayerNorm.weight\n",
      "bert.encoder.layer.10.output.LayerNorm.bias\n",
      "bert.encoder.layer.11.attention.self.query.weight\n",
      "bert.encoder.layer.11.attention.self.query.bias\n",
      "bert.encoder.layer.11.attention.self.key.weight\n",
      "bert.encoder.layer.11.attention.self.key.bias\n",
      "bert.encoder.layer.11.attention.self.value.weight\n",
      "bert.encoder.layer.11.attention.self.value.bias\n",
      "bert.encoder.layer.11.attention.output.dense.weight\n",
      "bert.encoder.layer.11.attention.output.dense.bias\n",
      "bert.encoder.layer.11.attention.output.LayerNorm.weight\n",
      "bert.encoder.layer.11.attention.output.LayerNorm.bias\n",
      "bert.encoder.layer.11.intermediate.dense.weight\n",
      "bert.encoder.layer.11.intermediate.dense.bias\n",
      "bert.encoder.layer.11.output.dense.weight\n",
      "bert.encoder.layer.11.output.dense.bias\n",
      "bert.encoder.layer.11.output.LayerNorm.weight\n",
      "bert.encoder.layer.11.output.LayerNorm.bias\n",
      "bert.pooler.dense.weight\n",
      "bert.pooler.dense.bias\n",
      "classifier.weight\n",
      "classifier.bias\n"
     ]
    }
   ],
   "source": [
    "# Print layer names\n",
    "for name, param in model.named_parameters():\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FnpGOry_Bm36"
   },
   "outputs": [],
   "source": [
    "for name, param in model.named_parameters():\n",
    "\n",
    "     # Trainable classification head\n",
    "     if name.startswith(\"classifier\"):\n",
    "        param.requires_grad = True\n",
    "\n",
    "      # Freeze everything else\n",
    "     else:\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1719386999026,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "vf_wYzpMB4uX",
    "outputId": "8a63f6d8-4516-4d84-d0d5-39f1823b4d03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter: bert.embeddings.word_embeddings.weight ----- False\n",
      "Parameter: bert.embeddings.position_embeddings.weight ----- False\n",
      "Parameter: bert.embeddings.token_type_embeddings.weight ----- False\n",
      "Parameter: bert.embeddings.LayerNorm.weight ----- False\n",
      "Parameter: bert.embeddings.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.0.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.0.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.1.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.1.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.2.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.2.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.3.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.3.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.4.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.4.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.5.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.5.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.6.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.6.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.7.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.7.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.8.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.8.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.9.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.9.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.10.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.10.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.query.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.query.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.key.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.key.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.value.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.self.value.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.intermediate.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.intermediate.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.output.dense.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.output.dense.bias ----- False\n",
      "Parameter: bert.encoder.layer.11.output.LayerNorm.weight ----- False\n",
      "Parameter: bert.encoder.layer.11.output.LayerNorm.bias ----- False\n",
      "Parameter: bert.pooler.dense.weight ----- False\n",
      "Parameter: bert.pooler.dense.bias ----- False\n",
      "Parameter: classifier.weight ----- True\n",
      "Parameter: classifier.bias ----- True\n"
     ]
    }
   ],
   "source": [
    "# We can check whether the model was correctly updated\n",
    "for name, param in model.named_parameters():\n",
    "     print(f\"Parameter: {name} ----- {param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 15480,
     "status": "ok",
     "timestamp": 1719387014504,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "rVi36FJSG4ue",
    "outputId": "c2e92d2d-247e-4065-8d06-688e24f472f7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='534' max='534' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [534/534 00:15, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.697000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=534, training_loss=0.6962381677234664, metrics={'train_runtime': 15.234, 'train_samples_per_second': 559.931, 'train_steps_per_second': 35.053, 'total_flos': 227605451772240.0, 'train_loss': 0.6962381677234664, 'epoch': 1.0})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "# Trainer which executes the training process\n",
    "trainer = Trainer(\n",
    "   model=model,\n",
    "   args=training_args,\n",
    "   train_dataset=tokenized_train,\n",
    "   eval_dataset=tokenized_test,\n",
    "   tokenizer=tokenizer,\n",
    "   data_collator=data_collator,\n",
    "   compute_metrics=compute_metrics,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 2623,
     "status": "ok",
     "timestamp": 1719387017125,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "eCPpixB1HCsI",
    "outputId": "3d77ed38-0565-492b-b488-09eb525fc316"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='67' max='67' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [67/67 00:01]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.6823198795318604,\n",
       " 'eval_f1': 0.637704918032787,\n",
       " 'eval_runtime': 2.7203,\n",
       " 'eval_samples_per_second': 391.865,\n",
       " 'eval_steps_per_second': 24.629,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uw729mLhIQL6"
   },
   "source": [
    "### Freeze blocks 1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 640,
     "status": "ok",
     "timestamp": 1719387017762,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "RbsLR561Kje-",
    "outputId": "ae1b3e9b-443d-4a06-94bb-6792d1751e27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter: 0bert.embeddings.word_embeddings.weight ----- False\n",
      "Parameter: 1bert.embeddings.position_embeddings.weight ----- False\n",
      "Parameter: 2bert.embeddings.token_type_embeddings.weight ----- False\n",
      "Parameter: 3bert.embeddings.LayerNorm.weight ----- False\n",
      "Parameter: 4bert.embeddings.LayerNorm.bias ----- False\n",
      "Parameter: 5bert.encoder.layer.0.attention.self.query.weight ----- False\n",
      "Parameter: 6bert.encoder.layer.0.attention.self.query.bias ----- False\n",
      "Parameter: 7bert.encoder.layer.0.attention.self.key.weight ----- False\n",
      "Parameter: 8bert.encoder.layer.0.attention.self.key.bias ----- False\n",
      "Parameter: 9bert.encoder.layer.0.attention.self.value.weight ----- False\n",
      "Parameter: 10bert.encoder.layer.0.attention.self.value.bias ----- False\n",
      "Parameter: 11bert.encoder.layer.0.attention.output.dense.weight ----- False\n",
      "Parameter: 12bert.encoder.layer.0.attention.output.dense.bias ----- False\n",
      "Parameter: 13bert.encoder.layer.0.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 14bert.encoder.layer.0.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 15bert.encoder.layer.0.intermediate.dense.weight ----- False\n",
      "Parameter: 16bert.encoder.layer.0.intermediate.dense.bias ----- False\n",
      "Parameter: 17bert.encoder.layer.0.output.dense.weight ----- False\n",
      "Parameter: 18bert.encoder.layer.0.output.dense.bias ----- False\n",
      "Parameter: 19bert.encoder.layer.0.output.LayerNorm.weight ----- False\n",
      "Parameter: 20bert.encoder.layer.0.output.LayerNorm.bias ----- False\n",
      "Parameter: 21bert.encoder.layer.1.attention.self.query.weight ----- False\n",
      "Parameter: 22bert.encoder.layer.1.attention.self.query.bias ----- False\n",
      "Parameter: 23bert.encoder.layer.1.attention.self.key.weight ----- False\n",
      "Parameter: 24bert.encoder.layer.1.attention.self.key.bias ----- False\n",
      "Parameter: 25bert.encoder.layer.1.attention.self.value.weight ----- False\n",
      "Parameter: 26bert.encoder.layer.1.attention.self.value.bias ----- False\n",
      "Parameter: 27bert.encoder.layer.1.attention.output.dense.weight ----- False\n",
      "Parameter: 28bert.encoder.layer.1.attention.output.dense.bias ----- False\n",
      "Parameter: 29bert.encoder.layer.1.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 30bert.encoder.layer.1.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 31bert.encoder.layer.1.intermediate.dense.weight ----- False\n",
      "Parameter: 32bert.encoder.layer.1.intermediate.dense.bias ----- False\n",
      "Parameter: 33bert.encoder.layer.1.output.dense.weight ----- False\n",
      "Parameter: 34bert.encoder.layer.1.output.dense.bias ----- False\n",
      "Parameter: 35bert.encoder.layer.1.output.LayerNorm.weight ----- False\n",
      "Parameter: 36bert.encoder.layer.1.output.LayerNorm.bias ----- False\n",
      "Parameter: 37bert.encoder.layer.2.attention.self.query.weight ----- False\n",
      "Parameter: 38bert.encoder.layer.2.attention.self.query.bias ----- False\n",
      "Parameter: 39bert.encoder.layer.2.attention.self.key.weight ----- False\n",
      "Parameter: 40bert.encoder.layer.2.attention.self.key.bias ----- False\n",
      "Parameter: 41bert.encoder.layer.2.attention.self.value.weight ----- False\n",
      "Parameter: 42bert.encoder.layer.2.attention.self.value.bias ----- False\n",
      "Parameter: 43bert.encoder.layer.2.attention.output.dense.weight ----- False\n",
      "Parameter: 44bert.encoder.layer.2.attention.output.dense.bias ----- False\n",
      "Parameter: 45bert.encoder.layer.2.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 46bert.encoder.layer.2.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 47bert.encoder.layer.2.intermediate.dense.weight ----- False\n",
      "Parameter: 48bert.encoder.layer.2.intermediate.dense.bias ----- False\n",
      "Parameter: 49bert.encoder.layer.2.output.dense.weight ----- False\n",
      "Parameter: 50bert.encoder.layer.2.output.dense.bias ----- False\n",
      "Parameter: 51bert.encoder.layer.2.output.LayerNorm.weight ----- False\n",
      "Parameter: 52bert.encoder.layer.2.output.LayerNorm.bias ----- False\n",
      "Parameter: 53bert.encoder.layer.3.attention.self.query.weight ----- False\n",
      "Parameter: 54bert.encoder.layer.3.attention.self.query.bias ----- False\n",
      "Parameter: 55bert.encoder.layer.3.attention.self.key.weight ----- False\n",
      "Parameter: 56bert.encoder.layer.3.attention.self.key.bias ----- False\n",
      "Parameter: 57bert.encoder.layer.3.attention.self.value.weight ----- False\n",
      "Parameter: 58bert.encoder.layer.3.attention.self.value.bias ----- False\n",
      "Parameter: 59bert.encoder.layer.3.attention.output.dense.weight ----- False\n",
      "Parameter: 60bert.encoder.layer.3.attention.output.dense.bias ----- False\n",
      "Parameter: 61bert.encoder.layer.3.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 62bert.encoder.layer.3.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 63bert.encoder.layer.3.intermediate.dense.weight ----- False\n",
      "Parameter: 64bert.encoder.layer.3.intermediate.dense.bias ----- False\n",
      "Parameter: 65bert.encoder.layer.3.output.dense.weight ----- False\n",
      "Parameter: 66bert.encoder.layer.3.output.dense.bias ----- False\n",
      "Parameter: 67bert.encoder.layer.3.output.LayerNorm.weight ----- False\n",
      "Parameter: 68bert.encoder.layer.3.output.LayerNorm.bias ----- False\n",
      "Parameter: 69bert.encoder.layer.4.attention.self.query.weight ----- False\n",
      "Parameter: 70bert.encoder.layer.4.attention.self.query.bias ----- False\n",
      "Parameter: 71bert.encoder.layer.4.attention.self.key.weight ----- False\n",
      "Parameter: 72bert.encoder.layer.4.attention.self.key.bias ----- False\n",
      "Parameter: 73bert.encoder.layer.4.attention.self.value.weight ----- False\n",
      "Parameter: 74bert.encoder.layer.4.attention.self.value.bias ----- False\n",
      "Parameter: 75bert.encoder.layer.4.attention.output.dense.weight ----- False\n",
      "Parameter: 76bert.encoder.layer.4.attention.output.dense.bias ----- False\n",
      "Parameter: 77bert.encoder.layer.4.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 78bert.encoder.layer.4.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 79bert.encoder.layer.4.intermediate.dense.weight ----- False\n",
      "Parameter: 80bert.encoder.layer.4.intermediate.dense.bias ----- False\n",
      "Parameter: 81bert.encoder.layer.4.output.dense.weight ----- False\n",
      "Parameter: 82bert.encoder.layer.4.output.dense.bias ----- False\n",
      "Parameter: 83bert.encoder.layer.4.output.LayerNorm.weight ----- False\n",
      "Parameter: 84bert.encoder.layer.4.output.LayerNorm.bias ----- False\n",
      "Parameter: 85bert.encoder.layer.5.attention.self.query.weight ----- False\n",
      "Parameter: 86bert.encoder.layer.5.attention.self.query.bias ----- False\n",
      "Parameter: 87bert.encoder.layer.5.attention.self.key.weight ----- False\n",
      "Parameter: 88bert.encoder.layer.5.attention.self.key.bias ----- False\n",
      "Parameter: 89bert.encoder.layer.5.attention.self.value.weight ----- False\n",
      "Parameter: 90bert.encoder.layer.5.attention.self.value.bias ----- False\n",
      "Parameter: 91bert.encoder.layer.5.attention.output.dense.weight ----- False\n",
      "Parameter: 92bert.encoder.layer.5.attention.output.dense.bias ----- False\n",
      "Parameter: 93bert.encoder.layer.5.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 94bert.encoder.layer.5.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 95bert.encoder.layer.5.intermediate.dense.weight ----- False\n",
      "Parameter: 96bert.encoder.layer.5.intermediate.dense.bias ----- False\n",
      "Parameter: 97bert.encoder.layer.5.output.dense.weight ----- False\n",
      "Parameter: 98bert.encoder.layer.5.output.dense.bias ----- False\n",
      "Parameter: 99bert.encoder.layer.5.output.LayerNorm.weight ----- False\n",
      "Parameter: 100bert.encoder.layer.5.output.LayerNorm.bias ----- False\n",
      "Parameter: 101bert.encoder.layer.6.attention.self.query.weight ----- False\n",
      "Parameter: 102bert.encoder.layer.6.attention.self.query.bias ----- False\n",
      "Parameter: 103bert.encoder.layer.6.attention.self.key.weight ----- False\n",
      "Parameter: 104bert.encoder.layer.6.attention.self.key.bias ----- False\n",
      "Parameter: 105bert.encoder.layer.6.attention.self.value.weight ----- False\n",
      "Parameter: 106bert.encoder.layer.6.attention.self.value.bias ----- False\n",
      "Parameter: 107bert.encoder.layer.6.attention.output.dense.weight ----- False\n",
      "Parameter: 108bert.encoder.layer.6.attention.output.dense.bias ----- False\n",
      "Parameter: 109bert.encoder.layer.6.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 110bert.encoder.layer.6.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 111bert.encoder.layer.6.intermediate.dense.weight ----- False\n",
      "Parameter: 112bert.encoder.layer.6.intermediate.dense.bias ----- False\n",
      "Parameter: 113bert.encoder.layer.6.output.dense.weight ----- False\n",
      "Parameter: 114bert.encoder.layer.6.output.dense.bias ----- False\n",
      "Parameter: 115bert.encoder.layer.6.output.LayerNorm.weight ----- False\n",
      "Parameter: 116bert.encoder.layer.6.output.LayerNorm.bias ----- False\n",
      "Parameter: 117bert.encoder.layer.7.attention.self.query.weight ----- False\n",
      "Parameter: 118bert.encoder.layer.7.attention.self.query.bias ----- False\n",
      "Parameter: 119bert.encoder.layer.7.attention.self.key.weight ----- False\n",
      "Parameter: 120bert.encoder.layer.7.attention.self.key.bias ----- False\n",
      "Parameter: 121bert.encoder.layer.7.attention.self.value.weight ----- False\n",
      "Parameter: 122bert.encoder.layer.7.attention.self.value.bias ----- False\n",
      "Parameter: 123bert.encoder.layer.7.attention.output.dense.weight ----- False\n",
      "Parameter: 124bert.encoder.layer.7.attention.output.dense.bias ----- False\n",
      "Parameter: 125bert.encoder.layer.7.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 126bert.encoder.layer.7.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 127bert.encoder.layer.7.intermediate.dense.weight ----- False\n",
      "Parameter: 128bert.encoder.layer.7.intermediate.dense.bias ----- False\n",
      "Parameter: 129bert.encoder.layer.7.output.dense.weight ----- False\n",
      "Parameter: 130bert.encoder.layer.7.output.dense.bias ----- False\n",
      "Parameter: 131bert.encoder.layer.7.output.LayerNorm.weight ----- False\n",
      "Parameter: 132bert.encoder.layer.7.output.LayerNorm.bias ----- False\n",
      "Parameter: 133bert.encoder.layer.8.attention.self.query.weight ----- False\n",
      "Parameter: 134bert.encoder.layer.8.attention.self.query.bias ----- False\n",
      "Parameter: 135bert.encoder.layer.8.attention.self.key.weight ----- False\n",
      "Parameter: 136bert.encoder.layer.8.attention.self.key.bias ----- False\n",
      "Parameter: 137bert.encoder.layer.8.attention.self.value.weight ----- False\n",
      "Parameter: 138bert.encoder.layer.8.attention.self.value.bias ----- False\n",
      "Parameter: 139bert.encoder.layer.8.attention.output.dense.weight ----- False\n",
      "Parameter: 140bert.encoder.layer.8.attention.output.dense.bias ----- False\n",
      "Parameter: 141bert.encoder.layer.8.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 142bert.encoder.layer.8.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 143bert.encoder.layer.8.intermediate.dense.weight ----- False\n",
      "Parameter: 144bert.encoder.layer.8.intermediate.dense.bias ----- False\n",
      "Parameter: 145bert.encoder.layer.8.output.dense.weight ----- False\n",
      "Parameter: 146bert.encoder.layer.8.output.dense.bias ----- False\n",
      "Parameter: 147bert.encoder.layer.8.output.LayerNorm.weight ----- False\n",
      "Parameter: 148bert.encoder.layer.8.output.LayerNorm.bias ----- False\n",
      "Parameter: 149bert.encoder.layer.9.attention.self.query.weight ----- False\n",
      "Parameter: 150bert.encoder.layer.9.attention.self.query.bias ----- False\n",
      "Parameter: 151bert.encoder.layer.9.attention.self.key.weight ----- False\n",
      "Parameter: 152bert.encoder.layer.9.attention.self.key.bias ----- False\n",
      "Parameter: 153bert.encoder.layer.9.attention.self.value.weight ----- False\n",
      "Parameter: 154bert.encoder.layer.9.attention.self.value.bias ----- False\n",
      "Parameter: 155bert.encoder.layer.9.attention.output.dense.weight ----- False\n",
      "Parameter: 156bert.encoder.layer.9.attention.output.dense.bias ----- False\n",
      "Parameter: 157bert.encoder.layer.9.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 158bert.encoder.layer.9.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 159bert.encoder.layer.9.intermediate.dense.weight ----- False\n",
      "Parameter: 160bert.encoder.layer.9.intermediate.dense.bias ----- False\n",
      "Parameter: 161bert.encoder.layer.9.output.dense.weight ----- False\n",
      "Parameter: 162bert.encoder.layer.9.output.dense.bias ----- False\n",
      "Parameter: 163bert.encoder.layer.9.output.LayerNorm.weight ----- False\n",
      "Parameter: 164bert.encoder.layer.9.output.LayerNorm.bias ----- False\n",
      "Parameter: 165bert.encoder.layer.10.attention.self.query.weight ----- False\n",
      "Parameter: 166bert.encoder.layer.10.attention.self.query.bias ----- False\n",
      "Parameter: 167bert.encoder.layer.10.attention.self.key.weight ----- False\n",
      "Parameter: 168bert.encoder.layer.10.attention.self.key.bias ----- False\n",
      "Parameter: 169bert.encoder.layer.10.attention.self.value.weight ----- False\n",
      "Parameter: 170bert.encoder.layer.10.attention.self.value.bias ----- False\n",
      "Parameter: 171bert.encoder.layer.10.attention.output.dense.weight ----- False\n",
      "Parameter: 172bert.encoder.layer.10.attention.output.dense.bias ----- False\n",
      "Parameter: 173bert.encoder.layer.10.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 174bert.encoder.layer.10.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 175bert.encoder.layer.10.intermediate.dense.weight ----- False\n",
      "Parameter: 176bert.encoder.layer.10.intermediate.dense.bias ----- False\n",
      "Parameter: 177bert.encoder.layer.10.output.dense.weight ----- False\n",
      "Parameter: 178bert.encoder.layer.10.output.dense.bias ----- False\n",
      "Parameter: 179bert.encoder.layer.10.output.LayerNorm.weight ----- False\n",
      "Parameter: 180bert.encoder.layer.10.output.LayerNorm.bias ----- False\n",
      "Parameter: 181bert.encoder.layer.11.attention.self.query.weight ----- False\n",
      "Parameter: 182bert.encoder.layer.11.attention.self.query.bias ----- False\n",
      "Parameter: 183bert.encoder.layer.11.attention.self.key.weight ----- False\n",
      "Parameter: 184bert.encoder.layer.11.attention.self.key.bias ----- False\n",
      "Parameter: 185bert.encoder.layer.11.attention.self.value.weight ----- False\n",
      "Parameter: 186bert.encoder.layer.11.attention.self.value.bias ----- False\n",
      "Parameter: 187bert.encoder.layer.11.attention.output.dense.weight ----- False\n",
      "Parameter: 188bert.encoder.layer.11.attention.output.dense.bias ----- False\n",
      "Parameter: 189bert.encoder.layer.11.attention.output.LayerNorm.weight ----- False\n",
      "Parameter: 190bert.encoder.layer.11.attention.output.LayerNorm.bias ----- False\n",
      "Parameter: 191bert.encoder.layer.11.intermediate.dense.weight ----- False\n",
      "Parameter: 192bert.encoder.layer.11.intermediate.dense.bias ----- False\n",
      "Parameter: 193bert.encoder.layer.11.output.dense.weight ----- False\n",
      "Parameter: 194bert.encoder.layer.11.output.dense.bias ----- False\n",
      "Parameter: 195bert.encoder.layer.11.output.LayerNorm.weight ----- False\n",
      "Parameter: 196bert.encoder.layer.11.output.LayerNorm.bias ----- False\n",
      "Parameter: 197bert.pooler.dense.weight ----- False\n",
      "Parameter: 198bert.pooler.dense.bias ----- False\n",
      "Parameter: 199classifier.weight ----- True\n",
      "Parameter: 200classifier.bias ----- True\n"
     ]
    }
   ],
   "source": [
    "# We can check whether the model was correctly updated\n",
    "for index, (name, param) in enumerate(model.named_parameters()):\n",
    "     print(f\"Parameter: {index}{name} ----- {param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "executionInfo": {
     "elapsed": 24489,
     "status": "ok",
     "timestamp": 1719387042249,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "QyleqOHICBjj",
    "outputId": "f2dc589c-1b8f-40e7-ef93-3ba0f3d3a3a3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='534' max='534' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [534/534 00:21, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.474600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='67' max='67' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [67/67 00:01]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.4092540740966797,\n",
       " 'eval_f1': 0.8141086749285034,\n",
       " 'eval_runtime': 2.7437,\n",
       " 'eval_samples_per_second': 388.523,\n",
       " 'eval_steps_per_second': 24.419,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load model\n",
    "model_id = \"bert-base-cased\"\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "# Encoder block 10 starts at index 165 and\n",
    "# we freeze everything before that block\n",
    "for index, (name, param) in enumerate(model.named_parameters()):\n",
    "    if index < 165:\n",
    "        param.requires_grad = False\n",
    "\n",
    "# Trainer which executes the training process\n",
    "trainer = Trainer(\n",
    "   model=model,\n",
    "   args=training_args,\n",
    "   train_dataset=tokenized_train,\n",
    "   eval_dataset=tokenized_test,\n",
    "   tokenizer=tokenizer,\n",
    "   data_collator=data_collator,\n",
    "   compute_metrics=compute_metrics,\n",
    ")\n",
    "trainer.train()\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HJRMWsLdA913"
   },
   "source": [
    "### [BONUS] Freeze blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ADvKJjNaFAot"
   },
   "outputs": [],
   "source": [
    "# scores = []\n",
    "# for index in range(12):\n",
    "#     # Re-load model\n",
    "#     model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
    "#     tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "#     # Freeze encoder blocks 0-index\n",
    "#     for name, param in model.named_parameters():\n",
    "#         if \"layer\" in name:\n",
    "#             layer_nr = int(name.split(\"layer\")[1].split(\".\")[1])\n",
    "#             if layer_nr <= index:\n",
    "#                 param.requires_grad = False\n",
    "#         else:\n",
    "#             param.requires_grad = True\n",
    "\n",
    "#     # Train\n",
    "#     trainer = Trainer(\n",
    "#       model=model,\n",
    "#       args=training_args,\n",
    "#       train_dataset=tokenized_train,\n",
    "#       eval_dataset=tokenized_test,\n",
    "#       tokenizer=tokenizer,\n",
    "#       data_collator=data_collator,\n",
    "#       compute_metrics=compute_metrics,\n",
    "#     )\n",
    "#     trainer.train()\n",
    "\n",
    "#     # Evaluate\n",
    "#     score = trainer.evaluate()[\"eval_f1\"]\n",
    "#     scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 212,
     "status": "ok",
     "timestamp": 1712321357732,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "dWYlHFNdLQtk",
    "outputId": "a28d645b-7633-4bdf-cc51-b7a937d36508"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8541862652869239,\n",
       " 0.8525519848771267,\n",
       " 0.8514664143803217,\n",
       " 0.8506616257088847,\n",
       " 0.8398104265402844,\n",
       " 0.8391345249294448,\n",
       " 0.8377358490566037,\n",
       " 0.8433962264150944,\n",
       " 0.8258801141769743,\n",
       " 0.816247582205029,\n",
       " 0.7917485265225934,\n",
       " 0.7019400352733686]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "executionInfo": {
     "elapsed": 1383,
     "status": "ok",
     "timestamp": 1712388601684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "bf3PIvKhOBJ-",
    "outputId": "aadf0ff7-e4ab-4d62-87b5-cbf1ecdea8da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# import numpy as np\n",
    "\n",
    "# # Create Figure\n",
    "# plt.figure(figsize=(8,4))\n",
    "\n",
    "# # Prepare Data\n",
    "# x = [f\"0-{index}\" for index in range(12)]\n",
    "# x[0] = \"None\"\n",
    "# x[-1] = \"All\"\n",
    "# y = [\n",
    "#     0.8541862652869239,\n",
    "#     0.8525519848771267,\n",
    "#     0.8514664143803217,\n",
    "#     0.8506616257088847,\n",
    "#     0.8398104265402844,\n",
    "#     0.8391345249294448,\n",
    "#     0.8377358490566037,\n",
    "#     0.8433962264150944,\n",
    "#     0.8258801141769743,\n",
    "#     0.816247582205029,\n",
    "#     0.7917485265225934,\n",
    "#     0.7019400352733686\n",
    "# ][::-1]\n",
    "\n",
    "# # Stylize Figure\n",
    "# plt.grid(color='#ECEFF1')\n",
    "# plt.axvline(x=4, color=\"#EC407A\", linestyle=\"--\")\n",
    "# plt.title(\"Effect of Frozen Encoder Blocks on Training Performance\")\n",
    "# plt.ylabel(\"F1-score\")\n",
    "# plt.xlabel(\"Trainable encoder blocks\")\n",
    "\n",
    "# # Plot Data\n",
    "# plt.plot(x, y, color=\"black\")\n",
    "\n",
    "# # Additional Annotation\n",
    "# plt.annotate(\n",
    "#     'Performance stabilizing',\n",
    "#     xy=(4, y[4]),\n",
    "#     xytext=(4.5, y[4]-.05),\n",
    "#     arrowprops=dict(\n",
    "#         arrowstyle=\"-|>\",\n",
    "#         connectionstyle=\"arc3\",\n",
    "#         color=\"#00ACC1\")\n",
    "# )\n",
    "# plt.savefig(\"multiple_frozen_blocks.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sf785lzMjwiy"
   },
   "source": [
    "## Few-shot Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8ybeQ3j6kOk4"
   },
   "outputs": [],
   "source": [
    "from setfit import sample_dataset\n",
    "\n",
    "# We simulate a few-shot setting by sampling 16 examples per class\n",
    "sampled_train_data = sample_dataset(tomatoes[\"train\"], num_samples=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3418,
     "status": "ok",
     "timestamp": 1719390247822,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "1Y55TDrmSqHm",
    "outputId": "84ec7a9c-92ed-4277-dfff-f0cb4cc918d6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from setfit import SetFitModel\n",
    "\n",
    "# Load a pre-trained SentenceTransformer model\n",
    "model = SetFitModel.from_pretrained(\"sentence-transformers/all-mpnet-base-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "630de7830edb4104938bef1c304129c0",
      "736c3004e2ec48b2b3a8974e465eb838",
      "54c6443169d84a3c9983d8c5b125c3e7",
      "bbee47201aac4b8f96aa772cc751d1d8",
      "9ad6e36197894003809d3ae6fa1b6a2c",
      "4708f67e67194377a94ec08aa07fe688",
      "c03a1bf3d2214c3680ab0b983dd582ef",
      "e777041b66fa484090714094d9516f35",
      "c5d50176a0a049769731d1c6fdb37f0f",
      "aa6d83c964d741178534cd0fae6ccec9",
      "318f91cb3cdd4f6e88681121de392207"
     ]
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1719390248689,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "zZ10SpAXdNvC",
    "outputId": "f9930b86-e077-4233-f7d7-16c5ec1e640d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "630de7830edb4104938bef1c304129c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/32 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from setfit import TrainingArguments as SetFitTrainingArguments\n",
    "from setfit import Trainer as SetFitTrainer\n",
    "\n",
    "# Define training arguments\n",
    "args = SetFitTrainingArguments(\n",
    "    num_epochs=3, # The number of epochs to use for contrastive learning\n",
    "    num_iterations=20  # The number of text pairs to generate\n",
    ")\n",
    "args.eval_strategy = args.evaluation_strategy\n",
    "\n",
    "# Create trainer\n",
    "trainer = SetFitTrainer(\n",
    "    model=model,\n",
    "    args=args,\n",
    "    train_dataset=sampled_train_data,\n",
    "    eval_dataset=test_data,\n",
    "    metric=\"f1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8HDP5-9wRYZS"
   },
   "outputs": [],
   "source": [
    "# from setfit import SetFitTrainer\n",
    "\n",
    "# # Create trainer\n",
    "# trainer = SetFitTrainer(\n",
    "#     model=model,\n",
    "#     train_dataset=sampled_train_data,\n",
    "#     eval_dataset=test_data,\n",
    "#     metric=\"f1\",\n",
    "#     num_epochs=3, # The number of epochs to use for contrastive learning\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "executionInfo": {
     "elapsed": 37840,
     "status": "ok",
     "timestamp": 1719390288851,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "3yaXeQJadWIS",
    "outputId": "3bb59731-1360-4f4f-eaae-989a8547ca7c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num unique pairs = 1280\n",
      "  Batch size = 16\n",
      "  Num epochs = 3\n",
      "  Total optimization steps = 240\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='240' max='240' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [240/240 00:37, Epoch 3/0]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training loop\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2455,
     "status": "ok",
     "timestamp": 1719390291303,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "PyRxiY32R3Jd",
    "outputId": "51d00a55-8086-4456-b3c2-d8720342bcc8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running evaluation *****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'f1': 0.8363988383349468}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on our test data\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1719390291304,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "-aKIHJpCQdAm",
    "outputId": "e2f786a4-78a4-4b92-d464-e3336d92edb2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model_head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L7NbUeSn-QSe"
   },
   "source": [
    "## MLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1601,
     "status": "ok",
     "timestamp": 1719387317479,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "Z35PD47AXXnv",
    "outputId": "45667c3b-9421-4406-e6e1-a406d803b8fd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
    "\n",
    "# Load model for Masked Language Modeling (MLM)\n",
    "model = AutoModelForMaskedLM.from_pretrained(\"bert-base-cased\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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      "1caf892f43c94d50bb31f12b830462c6",
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      "aa3c27c5a1254b19a7cfd04f28aee5d0",
      "c350f4d70d5646fc973aa9e68711f830",
      "1ca86de4b7174993942d43b0222f698f",
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      "933e2bdff863421abba4ba875a817dc0"
     ]
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1719387318933,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "zgLardIvEFTG",
    "outputId": "f40b1e95-9a82-437e-ffd8-5e0fdb4be138"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5333a40e5cbc4bb5b89628ecaa6608b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/8530 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83160c120ff64c42af6d38bb3fb932b5",
       "version_major": 2,
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      "text/plain": [
       "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "def preprocess_function(examples):\n",
    "   return tokenizer(examples[\"text\"], truncation=True)\n",
    "\n",
    "# Tokenize data\n",
    "tokenized_train = train_data.map(preprocess_function, batched=True)\n",
    "tokenized_train = tokenized_train.remove_columns(\"label\")\n",
    "tokenized_test = test_data.map(preprocess_function, batched=True)\n",
    "tokenized_test = tokenized_test.remove_columns(\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x7Yfg_2TECs_"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForLanguageModeling\n",
    "\n",
    "# Masking Tokens\n",
    "data_collator = DataCollatorForLanguageModeling(\n",
    "    tokenizer=tokenizer,\n",
    "    mlm=True,\n",
    "    mlm_probability=0.15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wqnA-ROy7ZRG"
   },
   "outputs": [],
   "source": [
    "# from transformers import DataCollatorForWholeWordMask\n",
    "\n",
    "# # Masking Whole Words\n",
    "# data_collator = DataCollatorForWholeWordMask(\n",
    "#     tokenizer=tokenizer,\n",
    "#     mlm=True,\n",
    "#     mlm_probability=0.15\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OduApRY03iOE"
   },
   "outputs": [],
   "source": [
    "# Training arguments for parameter tuning\n",
    "training_args = TrainingArguments(\n",
    "   \"model\",\n",
    "   learning_rate=2e-5,\n",
    "   per_device_train_batch_size=16,\n",
    "   per_device_eval_batch_size=16,\n",
    "   num_train_epochs=10,\n",
    "   weight_decay=0.01,\n",
    "   save_strategy=\"epoch\",\n",
    "   report_to=\"none\"\n",
    ")\n",
    "\n",
    "\n",
    "# Initialize Trainer\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_train,\n",
    "    eval_dataset=tokenized_test,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "executionInfo": {
     "elapsed": 731739,
     "status": "ok",
     "timestamp": 1719388053472,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "v-cRB3QmEiXl",
    "outputId": "3945078d-51b8-4ba9-9bd4-937cb4e7b858"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5340' max='5340' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [5340/5340 12:10, Epoch 10/10]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>2.601700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>2.377500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>2.313100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>2.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>2.150400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>2.096100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>2.059500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>1.990300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>1.986100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>1.958500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save pre-trained tokenizer\n",
    "tokenizer.save_pretrained(\"mlm\")\n",
    "\n",
    "# Train model\n",
    "trainer.train()\n",
    "\n",
    "# Save updated model\n",
    "model.save_pretrained(\"mlm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1511,
     "status": "ok",
     "timestamp": 1719388054975,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "HfxN1p8TOg2v",
    "outputId": "e9bcca59-cebb-4297-e675-b3d2a555d36e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> What a horrible idea!\n",
      ">>> What a horrible dream!\n",
      ">>> What a horrible thing!\n",
      ">>> What a horrible day!\n",
      ">>> What a horrible thought!\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "# Load and create predictions\n",
    "mask_filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
    "preds = mask_filler(\"What a horrible [MASK]!\")\n",
    "\n",
    "# Print results\n",
    "for pred in preds:\n",
    "    print(f\">>> {pred['sequence']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1719388054975,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "ogk1hJ4zOlAU",
    "outputId": "316b16d0-065b-41e8-c35c-3ce3be481469"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> What a horrible movie!\n",
      ">>> What a horrible film!\n",
      ">>> What a horrible mess!\n",
      ">>> What a horrible comedy!\n",
      ">>> What a horrible story!\n"
     ]
    }
   ],
   "source": [
    "# Load and create predictions\n",
    "mask_filler = pipeline(\"fill-mask\", model=\"mlm\")\n",
    "preds = mask_filler(\"What a horrible [MASK]!\")\n",
    "\n",
    "# Print results\n",
    "for pred in preds:\n",
    "    print(f\">>> {pred['sequence']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1sDqoG2NyeJO"
   },
   "source": [
    "## Named Entity Recognition\n",
    "\n",
    "Here are a number of interesting datasets you can also explore for NER:\n",
    "* tner/mit_movie_trivia\n",
    "* tner/mit_restaurant\n",
    "* wnut_17\n",
    "* conll2003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KGDvXU-ZzJ3J"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForTokenClassification, AutoTokenizer\n",
    "from transformers import DataCollatorWithPadding\n",
    "from transformers import TrainingArguments, Trainer\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298,
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     ]
    },
    "executionInfo": {
     "elapsed": 19287,
     "status": "ok",
     "timestamp": 1719388740030,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "YOQlMioIGY33",
    "outputId": "3f5e52d8-e736-411c-e468-d63b48828109"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8567cb28aff4974acf098b8fc4184ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4c076fd3de24dba84f4d566d4fca48a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The repository for conll2003 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conll2003.\n",
      "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
      "\n",
      "Do you wish to run the custom code? [y/N] y\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c9beb946034455e8f4e68cf9d3ed451",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec5ff2eceeaf425bb25a1580b1c86f35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d133524c37da40a688ae41d0be4fb608",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed3c3a380c3a46e9b6eabe8e0df7f7a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The CoNLL-2003 dataset for NER\n",
    "dataset = load_dataset(\"conll2003\", trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1719388740030,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "hOc5J7YgIPl8",
    "outputId": "648a5ac4-0e61-44fa-fea7-60d1af1853fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '848',\n",
       " 'tokens': ['Dean',\n",
       "  'Palmer',\n",
       "  'hit',\n",
       "  'his',\n",
       "  '30th',\n",
       "  'homer',\n",
       "  'for',\n",
       "  'the',\n",
       "  'Rangers',\n",
       "  '.'],\n",
       " 'pos_tags': [22, 22, 38, 29, 16, 21, 15, 12, 23, 7],\n",
       " 'chunk_tags': [11, 12, 21, 11, 12, 12, 13, 11, 12, 0],\n",
       " 'ner_tags': [1, 2, 0, 0, 0, 0, 0, 0, 3, 0]}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = dataset[\"train\"][848]\n",
    "example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1719388740030,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "hEPNSdADeAWD",
    "outputId": "fdb397ea-8105-44b2-b016-c29b69f61f7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O': 0,\n",
       " 'B-PER': 1,\n",
       " 'I-PER': 2,\n",
       " 'B-ORG': 3,\n",
       " 'I-ORG': 4,\n",
       " 'B-LOC': 5,\n",
       " 'I-LOC': 6,\n",
       " 'B-MISC': 7,\n",
       " 'I-MISC': 8}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label2id = {\n",
    "    'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4,\n",
    "    'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8\n",
    "}\n",
    "id2label = {index: label for label, index in label2id.items()}\n",
    "label2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 886,
     "status": "ok",
     "timestamp": 1719388740915,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "OtRCaz15AyjC",
    "outputId": "b22d94ad-b666-4f4c-a76b-07102752a88c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForTokenClassification\n",
    "\n",
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "# Load model\n",
    "model = AutoModelForTokenClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=len(id2label),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1719388740915,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "QOrlt03kIt_f",
    "outputId": "4f58559f-5d3e-43ed-a2f9-e6a5d13f5ba0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]',\n",
       " 'Dean',\n",
       " 'Palmer',\n",
       " 'hit',\n",
       " 'his',\n",
       " '30th',\n",
       " 'home',\n",
       " '##r',\n",
       " 'for',\n",
       " 'the',\n",
       " 'Rangers',\n",
       " '.',\n",
       " '[SEP]']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split individual tokens into sub-tokens\n",
    "token_ids = tokenizer(example[\"tokens\"], is_split_into_words=True)[\"input_ids\"]\n",
    "sub_tokens = tokenizer.convert_ids_to_tokens(token_ids)\n",
    "sub_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "2374a01e18eb42af8f9e9448c7500f3f",
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    },
    "executionInfo": {
     "elapsed": 4419,
     "status": "ok",
     "timestamp": 1712752415862,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "7s5A3mzj6TXf",
    "outputId": "b92cc409-96bd-43a0-9e3b-955003157f36"
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   "outputs": [
    {
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       "model_id": "2374a01e18eb42af8f9e9448c7500f3f",
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       "Map:   0%|          | 0/14041 [00:00<?, ? examples/s]"
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   "source": [
    "def align_labels(examples):\n",
    "    token_ids = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n",
    "    labels = examples[\"ner_tags\"]\n",
    "\n",
    "    updated_labels = []\n",
    "    for index, label in enumerate(labels):\n",
    "\n",
    "        # Map tokens to their respective word\n",
    "        word_ids = token_ids.word_ids(batch_index=index)\n",
    "        previous_word_idx = None\n",
    "        label_ids = []\n",
    "        for word_idx in word_ids:\n",
    "\n",
    "            # The start of a new word\n",
    "            if word_idx != previous_word_idx:\n",
    "\n",
    "                previous_word_idx = word_idx\n",
    "                updated_label = -100 if word_idx is None else label[word_idx]\n",
    "                label_ids.append(updated_label)\n",
    "\n",
    "            # Special token is -100\n",
    "            elif word_idx is None:\n",
    "                label_ids.append(-100)\n",
    "\n",
    "            # If the label is B-XXX we change it to I-XXX\n",
    "            else:\n",
    "                updated_label = label[word_idx]\n",
    "                if updated_label % 2 == 1:\n",
    "                    updated_label += 1\n",
    "                label_ids.append(updated_label)\n",
    "\n",
    "        updated_labels.append(label_ids)\n",
    "\n",
    "    token_ids[\"labels\"] = updated_labels\n",
    "    return token_ids\n",
    "\n",
    "tokenized = dataset.map(align_labels, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1712752662929,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "gIWOMzE-AhTu",
    "outputId": "66cfe4c4-406c-4362-d93f-7999078040f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: [1, 2, 0, 0, 0, 0, 0, 0, 3, 0]\n",
      "Updated: [-100, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, -100]\n"
     ]
    }
   ],
   "source": [
    "# Difference between original and updated labels\n",
    "print(f\"Original: {example['ner_tags']}\")\n",
    "print(f\"Updated: {tokenized['train'][848]['labels']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bA9nE7E6g97p"
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "# Load sequential evaluation\n",
    "seqeval = evaluate.load(\"seqeval\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    # Create predictions\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=2)\n",
    "\n",
    "    true_predictions = []\n",
    "    true_labels = []\n",
    "\n",
    "    # Document-level iteration\n",
    "    for prediction, label in zip(predictions, labels):\n",
    "\n",
    "      # token-level iteration\n",
    "      for token_prediction, token_label in zip(prediction, label):\n",
    "\n",
    "        # We ignore special tokens\n",
    "        if token_label != -100:\n",
    "          true_predictions.append([id2label[token_prediction]])\n",
    "          true_labels.append([id2label[token_label]])\n",
    "\n",
    "    results = seqeval.compute(predictions=true_predictions, references=true_labels)\n",
    "    return {\"f1\": results[\"overall_f1\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m0S4ZajdCaJl"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForTokenClassification\n",
    "\n",
    "# Token-classification Data Collator\n",
    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 170605,
     "status": "ok",
     "timestamp": 1712753138553,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "S76kRYZtBr5c",
    "outputId": "8c2e866b-e9a9-49e3-ef08-56aab5aacdb4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='878' max='878' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [878/878 02:49, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.047500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=878, training_loss=0.04094860494001037, metrics={'train_runtime': 169.4752, 'train_samples_per_second': 82.85, 'train_steps_per_second': 5.181, 'total_flos': 351240792638148.0, 'train_loss': 0.04094860494001037, 'epoch': 1.0})"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training arguments for parameter tuning\n",
    "training_args = TrainingArguments(\n",
    "   \"model\",\n",
    "   learning_rate=2e-5,\n",
    "   per_device_train_batch_size=16,\n",
    "   per_device_eval_batch_size=16,\n",
    "   num_train_epochs=1,\n",
    "   weight_decay=0.01,\n",
    "   save_strategy=\"epoch\",\n",
    "   report_to=\"none\"\n",
    ")\n",
    "\n",
    "# Initialize Trainer\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized[\"train\"],\n",
    "    eval_dataset=tokenized[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 14521,
     "status": "ok",
     "timestamp": 1712753153056,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "ds5osPr9T0pq",
    "outputId": "28e6836f-5eae-43c2-a37f-b92acb9a42b6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='216' max='216' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [216/216 00:09]\n",
       "    </div>\n",
       "    "
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.16888542473316193,\n",
       " 'eval_f1': 0.9180087380808113,\n",
       " 'eval_runtime': 14.5731,\n",
       " 'eval_samples_per_second': 236.943,\n",
       " 'eval_steps_per_second': 14.822,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on our test data\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2674,
     "status": "ok",
     "timestamp": 1712753204334,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "Q0PAXyzT-N45",
    "outputId": "d29825c0-2be2-48dd-c685-fad114afb369"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'entity': 'B-PER',\n",
       "  'score': 0.99534035,\n",
       "  'index': 4,\n",
       "  'word': 'Ma',\n",
       "  'start': 11,\n",
       "  'end': 13},\n",
       " {'entity': 'I-PER',\n",
       "  'score': 0.9928328,\n",
       "  'index': 5,\n",
       "  'word': '##arte',\n",
       "  'start': 13,\n",
       "  'end': 17},\n",
       " {'entity': 'I-PER',\n",
       "  'score': 0.9954301,\n",
       "  'index': 6,\n",
       "  'word': '##n',\n",
       "  'start': 17,\n",
       "  'end': 18}]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "# Save our fine-tuned model\n",
    "trainer.save_model(\"ner_model\")\n",
    "\n",
    "# Run inference on the fine-tuned model\n",
    "token_classifier = pipeline(\n",
    "    \"token-classification\",\n",
    "    model=\"ner_model\",\n",
    ")\n",
    "token_classifier(\"My name is Maarten.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MAfBhqVwC61e"
   },
   "outputs": [],
   "source": []
  }
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